This document discusses a framework called SPEAR that simultaneously estimates the positions of signal sources and uncertain anchors to improve source localization performance in the presence of network topology uncertainty. It focuses on theoretically assessing SPEAR's performance by deriving fundamental lower bounds using the Cramér–Rao information inequality. The results provide insight into the different position information components in the observed data and how they contribute toward overall position information. The interpretations can serve as a benchmark for location-aware systems operating with uncertain anchor positions. The document also provides contact information for Logic Mind Technologies to discuss further details.

1. Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Cramér–Rao Lower Bounds of RSS-Based
Localization with Anchor Position Uncertainty
Abstract
The location awareness is useful in many wireless networking solutions, such as cellular, ad hoc,
self-organizing, cognitive radio networks, and so on. The RSS-based, non-Bayesian source
localization is of particular interest, due to the presence of the RSS measurements in all radio
devices, as well as the instantaneous estimations without extensive training and learning phases.
However, most RSS-based localization algorithms usually neglect problems arising from
inherent network topology uncertainty. The positions of some measuring anchors, usually
assumed to be precisely known in advance, are often a subject of previous estimation, which
propagates errors in the source localization procedure and results in performance degradation. A
joint localization framework, termed source position estimation for anchor position uncertainty
reduction (SPEAR), has been recently proposed as a possible solution to the problem of RSS-
based localization in the presence of network topology uncertainty. The framework
simultaneously estimates the positions of the sources and the uncertain anchors and serves as
robust tool that provides improved source localization performance and more reliable anchor
position estimates. This paper focuses on the theoretical assessment of SPEAR’s performance by
deriving its fundamental lower bounds using the Cramér–Rao information inequality. The main
contribution of this paper is the detailed information-theoretic interpretation of the obtained
results. The interpretation provides valuable insight into the essence of the SPEAR localization
problem because it identifies different position information components in the observed data,
discovers how they are mutually related, and determines how they contribute toward the
resulting position information. This paper also provides geometric interpretation of the obtained
results and illustrates the principles of distribution of position information in space. The
theoretical results as well as their information-theoretic and geometric interpretations can be used

2. as a benchmark for RSS-based location-aware systems that operate in the presence of network
topology uncertainty.
SYSTEM SPECIFICATION
Software Specification
Operating system : Linux (Ubuntu)
Software : Network Simulator 2.35
Programming languages : Tool Command Language, AWK, C++
Hardware Specification
Main processor : Dual Core
Hard disk capacity : 80GB
Cache memory : 2 GB
PROJECT FLOW:
First Review:
Literature Survey
Paper Explanation
Design of Project
Project Enhancement explanation
Second Review:
Implementing 40% of Base Paper
Third Review
Implementing Remaining 60% of Base Paper with Future Enhancement (Modification)
For More Details please contact
Logic Mind Technologies
Vijayangar (NearMaruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Mail: logicmindtech@gmail.com